Project Summary/Abstract Despite significant advances in ventilator management, mortality from the acute respiratory distress syn- drome (ARDS) remains unacceptably high. Mechanical ventilation with large tidal volume, high pressure ven- tilation, and repeated alveolar collapse can injure the lung, called ventilator induced lung injury (VILI). Defined as the inappropriate timing and delivery of a breath in response to a patient effort, ventilator dyssynchrony (VD) may potentiate VILI. This proposal outlines a 5-year training programing including mentoring, formal didactics, and practical research experiences which positions Dr. Sottile for a successful clinical research career examining ventilator dyssynchrony (VD), its impact on ventilator induced lung injury (VILI), and its optimal management. An integrated curriculum will optimize an automated VD detection algorithm, delineate which types of VD are deleterious, and determine the ideal ventilator and sedation strategies to minimize VD and improve patient outcomes. This experience will provide Dr. Sottile with the necessary tools to be a leader in signal analysis, ventilator dyssynchrony, advanced mathematical modeling, and critical care research. This program will consist of formal mentoring from four renowned experts in machine learning, mechanical ventilation, critical care research, and modeling of dynamic systems. In addition, coursework in clinical sciences, machine learning, and advanced mathematical modeling will build the theoretical foundation to apply these techniques. This structured curriculum will help Dr. Sottile gain expertise in the computerized detection of VD from real-time ventilator data, the pathophysiology of VD, and the modeling of complex, temporally dynamic, biological systems. The formal curriculum will coincide with three practical experiences. First, Dr. Sottile will optimize his already developed VD identification algorithm to detect additional types of VD that may be injurious to the lung. Second, he will identify which types of VD are associated with deleterious ventilator mechanics. Finally, he will deter- mine the personalized ventilator and sedation strategies to minimize VD, VILI, and the negative consequences of over sedation using linear optimization and lagged linear correlation to account for the dynamic nature of patient physiology. The result of the proposed studies will develop a computerized algorithm to detect seven types of VD, identify which types of VD are most likely to propagate VILI, and determine the optimal ventilator and sedation strategies to improve individual patient outcomes. This will leave Dr. Sottile and the study team positioned to conduct a randomized clinical trial comparing computer-predicted individualized ventilator and se- dation strategies compared to the current standard of care. If successful, this will potentially to revolutionize the use of mechanical ventilation by tailoring evidenced-based therapies to the individual patient.